1. Field of Invention
The present invention relates generally to systems that create predictions or lists of recommended items for users based on the ratings of other users. In particular, the present invention relates to a collaborative filtering system that utilizes social networks. The predictions can be used by the system to select which items to show to users, or by users to select which items from a list to peruse.
2. Background of the Invention
Automated collaborative filtering (ACF) systems have been developed to predict the preferences of a user on various items, based on known attributes of that user as well as known attributes of other users. These attributes may contain age, gender, and income, as well as one or more of the user's known preferences. For example, a known preference of a user may be his enjoyment of the movie ‘Scarface.’
Several automated collaborative filtering engines are currently available. A discussion of such engines can be found, for example, in U.S. Pat. No. 4,870,579 (“System and Method of Predicting Subjective Reactions,” 1989) and U.S. Pat. No. 4,996,642 (“System and Method for Recommending Items,” 1991), both issued to John B. Hey (employed by LikeMinds Inc.), and Jonathan Herlocker et al., “An Algorithmic Framework for Performing Collaborative Filtering,” in Proceedings of the 1999 Conference on Research and Development in Information Retrieval.
In neighbor-based ACF, a user's preference is predicted based on the preferences of other users and weighted by their similarities. Two users' similarity are measured based on the similarity of their attributes. In the conventional approach, a user's preference is predicted using only her k nearest neighbors (that is, the k most similar users). Alternatively, a user's preference can be predicted based on all the neighbors or just those neighbors whose similarities are above a certain threshold.
It should be noted that conventional ACF systems suffer from the new-user and cold-start problems. Both of which lead to inaccuracy in computing the similarity of neighbors in the neighbor-based approach.
A new user to the system may have only provided a small set of her preferences. Thus there is often not sufficient information to truly differentiate between random users versus users who are similar to her. The calculation of the similarity between her and the other users may therefore be inaccurate, and the resulting prediction and recommendation can be erroneous.
The cold-start problem occurs when an ACF system is initially deployed, and most users are new users who have not provided a significant number of their preferences. In this situation, even when a new user is willing to provide many of her preferences, there is still not enough known about the other users to accurately calculate her similarity with those users. Thus the resulting prediction and recommendation can be inaccurate.
Note that model-based ACF systems, such as U.S. Pat. No. 5,704,017 (“Collaborative Filtering Utilizing a Belief Network,” 1997) issued to Heckerman et al., differ from neighbor-based ACF systems in that they perform collaborative filtering without calculating the similarity between users. Rather, these model-based systems build a probabilistic model from all users' preferences, and that model is applied to an active user to predict her unstated preferences based on her stated preferences. However, these model-based ACF systems still suffer from the new-user and cold-start problems. Under the new-user problem, the system still does not know enough about a user based just on the small amount of preferences she has given. Model-based ACF systems suffer from the cold-start problem because they cannot build an accurate model based on limited preference data during initial deployment.
U.S. Pat. No. 6,389,372 to Natalie S. Glance and Manfred Dardenne (2002) describes a collaborative filtering technique that bootstraps itself with information about relationships between users in a pre-determined organization. The organizational relationships influence the recommendations only when the users have provided few ratings. As more ratings are gathered, the organizational and network information becomes irrelevant. This is undesirable in many situations, as organizational and network relationships can provide additional information that cannot be derived from ratings alone, and this additional information can improve recommendation.
The invention in U.S. Pat. No. 6,389,372 utilizes an organizational structure that is pre-determined and therefore static. This does not apply to most social networks and organizational structures, which are dynamic. These networks can grow and change. The invention in U.S. Pat. No. 6,389,372 thus fails to provide accurate recommendation once the organization has changed from the pre-determined version used for bootstrapping.
In addition, for the invention in U.S. Pat. No. 6,389,372, the correlation values between users need to be stored for incremental update. For large social networks such memory storage can be prohibitive.
There are many commercial applications of ACF. For example, it has been used for Web advertising and e-commerce (Amazon.com, BarnesandNoble.com, etc.). The commercial value of these ACF applications monotonically increases with the system's accuracy; that is, the more accurate the prediction, the more valuable the system. Thus improving the accuracy of prediction, by incorporating more sources of information, is always desirable, both in the initial deployment and mature operation of the ACF application.